Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

Towards video streaming in IoT environments: vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues

Game theory framework for MAC parameter optimization in energy-delay constrained sensor networks

Optimizing energy consumption and end-to-end (e2e) packet delay in energy-constrained, delay-sensitive wireless sensor networks is a conflicting multiobjective optimization problem. We investigate the problem from a game theory perspective, where the two optimization objectives are considered as game players. The cost model of each player is mapped through a generalized optimization framework onto protocol-specific MAC parameters. From the optimization framework, a game is first defined by the Nash bargaining solution (NBS) to assure energy consumption and e2e delay balancing. Secondy, the Kalai-Smorodinsky bargaining solution (KSBS) is used to find an equal proportion of gain between players. Both methods offer a bargaining solution to the duty-cycle MAC protocol under different axioms. As a result, given the two performance requirements (i.e., the maximum latency tolerated by the application and the initial energy budget of nodes), the proposed framework allows to set tunable system parameters to reach a fair equilibrium point that dually minimizes the system latency and energy consumption. For illustration, this formulation is applied to six state-of-the-art wireless sensor network (WSN) MAC protocols: B-MAC, X-MAC, RI-MAC, SMAC, DMAC, and LMAC. The article shows the effectiveness and scalability of such a framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

[EN] Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.Kumar, K.; Kumar, S.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N. (2017). Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities. Energies. 10(12):1-40. https://doi.org/10.3390/en10122073S1401012Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. 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IEEE Cloud Computing, 4(1), 10-15. doi:10.1109/mcc.2017.5Tröster, G. (2011). Smart Clothes—The Unfulfilled Pledge? IEEE Pervasive Computing, 10(2), 87-89. doi:10.1109/mprv.2011.32Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376. doi:10.1109/comst.2015.2444095Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal, 4(5), 1125-1142. doi:10.1109/jiot.2017.2683200Perera, C., Liu, C. H., Jayawardena, S., & Min Chen. (2014). A Survey on Internet of Things From Industrial Market Perspective. IEEE Access, 2, 1660-1679. doi:10.1109/access.2015.2389854Kamilaris, A., & Pitsillides, A. (2016). Mobile Phone Computing and the Internet of Things: A Survey. IEEE Internet of Things Journal, 3(6), 885-898. doi:10.1109/jiot.2016.2600569Arcadius Tokognon, C., Gao, B., Tian, G. Y., & Yan, Y. (2017). Structural Health Monitoring Framework Based on Internet of Things: A Survey. IEEE Internet of Things Journal, 4(3), 619-635. doi:10.1109/jiot.2017.2664072Razzaque, M. A., Milojevic-Jevric, M., Palade, A., & Clarke, S. (2016). Middleware for Internet of Things: A Survey. IEEE Internet of Things Journal, 3(1), 70-95. doi:10.1109/jiot.2015.2498900Luong, N. C., Hoang, D. T., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2016). Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey. IEEE Communications Surveys & Tutorials, 18(4), 2546-2590. doi:10.1109/comst.2016.2582841Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 16(1), 414-454. doi:10.1109/surv.2013.042313.00197Khan, A. A., Rehmani, M. H., & Rachedi, A. (2017). Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions. IEEE Wireless Communications, 24(3), 17-25. doi:10.1109/mwc.2017.1600404Ahmed, E., Yaqoob, I., Gani, A., Imran, M., & Guizani, M. (2016). Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wireless Communications, 23(5), 10-16. doi:10.1109/mwc.2016.7721736Cao, Y., Jiang, T., & Han, Z. (2016). A Survey of Emerging M2M Systems: Context, Task, and Objective. IEEE Internet of Things Journal, 3(6), 1246-1258. doi:10.1109/jiot.2016.2582540Rajandekar, A., & Sikdar, B. (2015). A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications. IEEE Internet of Things Journal, 2(2), 175-186. doi:10.1109/jiot.2015.2394438Botta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684-700. doi:10.1016/j.future.2015.09.021Risteska Stojkoska, B. L., & Trivodaliev, K. V. (2017). A review of Internet of Things for smart home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464. doi:10.1016/j.jclepro.2016.10.006Liu, C. H., Fan, J., Branch, J. W., & Leung, K. K. (2014). Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments. IEEE Transactions on Emerging Topics in Computing, 2(4), 473-487. doi:10.1109/tetc.2014.2364915Du, R., Gkatzikis, L., Fischione, C., & Xiao, M. (2015). Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing. IEEE Journal on Selected Areas in Communications, 33(12), 2997-3010. doi:10.1109/jsac.2015.2481199Chen, Y., Chiotellis, N., Chuo, L.-X., Pfeiffer, C., Shi, Y., Dreslinski, R. G., … Kim, H. S. (2016). Energy-Autonomous Wireless Communication for Millimeter-Scale Internet-of-Things Sensor Nodes. IEEE Journal on Selected Areas in Communications, 34(12), 3962-3977. doi:10.1109/jsac.2016.2612041Akgül, Ö. U., & Canberk, B. (2016). Self-Organized Things (SoT): An energy efficient next generation network management. Computer Communications, 74, 52-62. doi:10.1016/j.comcom.2014.07.004Ahn, J. H., & Lee, T.-J. (2018). ALLYS: All You can Send for Energy Harvesting Networks. IEEE Transactions on Mobile Computing, 17(4), 775-788. doi:10.1109/tmc.2017.2740929Mondal, S., & Paily, R. (2017). Efficient Solar Power Management System for Self-Powered IoT Node. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(9), 2359-2369. doi:10.1109/tcsi.2017.2707566Qureshi, F. F., Iqbal, R., & Asghar, M. N. (2017). Energy efficient wireless communication technique based on Cognitive Radio for Internet of Things. Journal of Network and Computer Applications, 89, 14-25. doi:10.1016/j.jnca.2017.01.003Nguyen, T. D., Khan, J. Y., & Ngo, D. T. (2017). Energy harvested roadside IEEE 802.15.4 wireless sensor networks for IoT applications. Ad Hoc Networks, 56, 109-121. doi:10.1016/j.adhoc.2016.12.003Khanouche, M. E., Amirat, Y., Chibani, A., Kerkar, M., & Yachir, A. (2016). Energy-Centered and QoS-Aware Services Selection for Internet of Things. IEEE Transactions on Automation Science and Engineering, 13(3), 1256-1269. doi:10.1109/tase.2016.2539240Afzal, B., Alvi, S. A., Shah, G. A., & Mahmood, W. (2017). Energy efficient context aware traffic scheduling for IoT applications. Ad Hoc Networks, 62, 101-115. doi:10.1016/j.adhoc.2017.05.001Song, L., Chai, K. K., Chen, Y., Schormans, J., Loo, J., & Vinel, A. (2017). QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems. IEEE Systems Journal, 11(3), 1447-1455. doi:10.1109/jsyst.2015.2465292Energy-Efficient Probabilistic Routing Algorithm for Internet of Thingshttp://www.ietf.org/rfc/rfc3561.txtMachado, K., Rosário, D., Cerqueira, E., Loureiro, A., Neto, A., & de Souza, J. (2013). A Routing Protocol Based on Energy and Link Quality for Internet of Things Applications. Sensors, 13(2), 1942-1964. doi:10.3390/s130201942Chelloug, S. A. (2015). Energy-Efficient Content-Based Routing in Internet of Things. Journal of Computer and Communications, 03(12), 9-20. doi:10.4236/jcc.2015.312002Zhao, M., Ho, I. W.-H., & Chong, P. H. J. (2016). An Energy-Efficient Region-Based RPL Routing Protocol for Low-Power and Lossy Networks. IEEE Internet of Things Journal, 3(6), 1319-1333. doi:10.1109/jiot.2016.2593438Qiu, T., Lv, Y., Xia, F., Chen, N., Wan, J., & Tolba, A. (2016). 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Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

Quality-of-Information Aware Sensing Node Characterisation for Optimised Energy Consumption in Visual Sensor Networks

Energy consumption is one of the primary concerns in a resource constrained visual sensor network (VSN) with wireless transceiving capability. The existing VSN design solutions under particular resource constrained scenarios are application-specific, whereas the degree of sensitivity of the resource constraints varies from one application to another. This limits the implementation of the existing energy efficient solutions within a VSN node, which may be considered to be a part of a heterogeneous network. This thesis aims to resolve the energy consumption issues faced within VSNs because of their resource constrained nature by proposing energy efficient solutions for sensing nodes characterisation. The heterogeneity of image capture and processing within a VSN can be adaptively reflected with a dynamic field-of-view (FoV) realisation. This is expected to allow the implementation of a generalised energy efficient solution that will adapt with the heterogeneity of the network. In this thesis, a FoV characterisation framework is proposed, which can assist design engineers during the pre-deployment phase in developing energy efficient VSNs. The proposed FoV characterisation framework provides efficient solutions for: 1) selecting suitable sensing range; 2) maximising spatial coverage; 3) minimising the number of required nodes; and 4) adaptive task classification. The task classification scheme proposed in this thesis exploits heterogeneity of the network and leads to an optimal distribution of tasks between visual sensing nodes. Soft decision criteria is exploited, and it is observed that for a given detection reliability, the proposed FoV characterisation framework provides energy efficient solutions which can be implemented within heterogeneous networks. In the post-deployment phase, the energy efficiency of a VSN for a given level of reliability can be enhanced by reconfiguring its nodes dynamically to achieve optimal configurations. Considering the dynamic realisation of quality-of-information (QoI), a strategy is devised for selecting suitable configurations of visual sensing nodes to reduce redundant visual content prior to transmission without sacrificing the expected information retrieval reliability. By incorporating QoI awareness using peak signal-to-noise ratio-based representative metric, the distributed nature of the proposed self-reconfiguration scheme accelerates the decision making process. This thesis also proposes a unified framework for node classification and dynamic self-reconfiguration in VSNs. For a given application, the unified framework provides a feasible solution to classify and reconfigure visual sensing nodes based on their FoV by exploiting the heterogeneity of targeted QoI within the sensing region. From the results, it is observed that for the second degree of heterogeneity in targeted QoI, the unified framework outperforms its existing counterparts and results in up to 72% energy savings with as low as 94% reliability. Within the context of resource constrained VSNs, the substantial energy savings achieved by the proposed unified framework can lead to network lifetime enhancement. Moreover, the reliability analysis demonstrates suitability of the unified framework for applications that need a desired level of QoI

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms